Simulating Brushstrokes with Big Data

A fundamental component of paintings, are the brushstrokes, which can create a sense of texture and depth. Recreating them with computers has proven very difficult though, because of their complexity. Researchers at Princeton University however have found a way to bring brushstrokes to computers using Big Data.

Called RealBrush, the prototype software employs machine learning to identify the features of a library of exemplar brushstrokes. By analyzing the spine of the brushstrokes, the software is able to determine their shape, and from that, warp and blend segments to generate any shape desired. It is also able to smear and smudge the brushstroke, using a similar approach. If the library does not have the necessary brushstrokes, RealBrush is able to accept photographs of real brushstrokes and add those to its library. This use of Big Data is a growing trend for modern computing, when mathematical algorithms simply do not suffice.

Though RealBrush is ready for use in the lab, it is not ready for anything outside of it. As the researchers point out, it is still a research project and would require more effort before it could be made available.